Maintenance Management Process in the Electricity Distribution Business: Analytic Models. June 2014

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June 2014

Maintenance

Management Process in

the Electricity

Distribution Business:

Analytic Models

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Asset management

Available information

Impact on the service level:

• Availability. • Service interruption. • Number of interruptions. • Malfunction. • Breaking. • Loss of service. • Replacement.

• Reference value for parameters with respect to fault conditions.

• Manufacturer's instructions. • Load. • Circulated energy. • Voltage level supported. • Actions, protections. • Temperature. • Oil composition. • Shoots. Units Total cost (thousand of €) Unit cost (thousand of €) Aerial MVL 27.956,00 70.318,00 2,52 CT 37.642,00 55.718,00 1,48 Aerial RBT 32.491,00 52.207,00 1,61

Substation - Position with conventional switch 1.915,00 31.158,00 16,27 Substation - Position with bunkered switch 4.123,00 19.908,00 4,83

Aerial HTL 7.348,00 17.550,00 2,39

Transformers 1.322,00 15.074,00 11,40

Underground RBT 12.103,00 13.302,00 1,10

Underground MTL 10.248,00 13.265,00 1,29

Underground HTL 667,00 4.069,00 6,10

Mobile equipment - Positon with bunkered switch 343,00 500,00 1,46 Substation - Position with conventional switch 252,00 372,00 1,48 Substation - Position with bunkered switch 169,00 57,00 0,34

Capacitors 53,00 57,00 1,08

Mobile equipment - Positon without conventional switch 29,00 51,00 1,76 Mobile equipment - Positon with conventional switch 12,00 27,00 2,25 Mobile equipment - Positon without bunkered switch 10,00 4,00 0,40

Types of maintenance Schedules:

• Tasks and frequency.

• Resource consumption.

• Age of the equipment.

• Consumption. • Performance. 0 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 P ro d u cció n Tiempo Producción-Predictivo Producción-No predictivo Time P ro d u c tio n _{Production –} Predictive Production – Non-Predictive

Interventions

Locations

and

equipment

Equipment

activity

parameters

Service level

Fault record

Costs

Activity

levels/

performance

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Asset management

The business process

Data acquisition Data integration and cleaning Analysis Warnings Resource allocation Management model Logistic process and ranges optimization Data management Analyze and predict Understand, react and adapt Optimize processes Performance assessment

Measure

and

manage

Data

integration

Maintenance

strategy

Optimization

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Asset management

Analytic model

Data. Business information. Information. Processed data. Assessment. Information for decision- making.

How will we organise maintenance schedules

to reduce costs?

How much income do we waste on account of unnecesary maintenance actions? Which equipment breaks down more often than

normal? Which are the patterns

of failure? Description

 Performance metrics.

 Warnings.

 Management and reports.

Predictive

 Management algorithms.

 Modelling and simulation.

 Quantitative analysis.  Forecast. Processable intelligence Information for Predictive Intelligence

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Asset management

Analytic model

Advantages of Analytics

Take advantage of the big amount of stored data, not monitored

nowadays.

Include an estimation of the failure probability in the management

process and evaluate the economic impact of these failures: reduce

costs by means of better decisions based on analytical data.

Optimize management: minimize maintenance costs, minimize

downtime, choose the best between repair or replace equipment,…

Get relevant information to make decisions about maintenance

policies and to negotiate contracts.

Anticipate new business challenges and improve the response to

them.

Automate responses to make processes more efficient.

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Asset management

Analytic model

Decision process Control panels Data: On-going monitoring In te gr at ion of t h e in for m at ion Asset management database Information on incidents /breakdowns Information from manufacturer Equipment performance (variables) Maintenance actions Direct cost of maintenance Lost profits Analytic models: • Failure probability according to value of variables • Failure forecast • Economic assessment: Costs and lost profits

Control panels

Failure probability according to value

of variables

Failure forecast for 6 months, 1 year, 18 months,… Simulation of the economic impact of different failure modes: potential damages and loss

of profit What-if analysis: economic assessment according to maintenance actions A n tic ip a tio n o f M a in te n a n c e a n d L o g is tic s p ro c e s s e s Component criticality Condition-based maintenance Delay or catch up on maintenance actions Investment or replacement decisions Maintenance schedules updating Decisions on maintenance policies Conditions to contract services Standards on replacement parts Bu d g e tin g a n d m a n a g e m e n t c o n tr o l p ro c e s s e s Evaluate stopping procedures

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Asset management

Information model

Activity data measured by sensors Asset management database

Maintenance management system

Incident management system

Equipment downtime Actions Maintenance costs: contractors, materials, replacements,… Number of downtimes

SCAD

A

Calculation platform Failure analysis Economic analysis Risk of failure simulator Control panels

Monitoring of technic and economic variables Technic variable values: monitoring Cost evolution Availability evolution Failure pattern Management of the information obtained from

the calculation platform

Simulation of the economic impact of failure models: potential damage and lost profits Simulation of maintenance strategies Component criticality Failure forecast Failure probability Vibrations Temperature Over excitation Oil levels Oil density Oil viscosity Voltage in the generator Water inlet pressure to

the converter refrigerator

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Failure profile

Asset management

Information model

e n e -10 fe b -10 m ar -10 ab r-1 0 m ay -10 ju n -10 ju l-1 0 ago -10 se p -1 0 o ct -10 n o v-10 d ic-10 e n e -11 fe b -11 m ar -11 ab r-1 1 m ay -11 ju n -11 ju l-1 1 ago -11 se p -1 1 o ct -11 n o v-11 d ic-11 e n e -12 fe b -12 m ar -12 ab r-1 2 m ay -12 ju n -12 ju l-1 2 ago -12 se p -1 2 o ct -12 n o v-12 d ic-12

Fuego o explosión Contaminación del aceite por gas Rayos Fallo aislamiento Fallo de diseño, material, humano Sobrecarga Mantenimiento inadecuado Inundación

Fire or explosion

Design, material, or human fault

Oil contamination from gas Overload Lighting (thunderstorm) Inadequate maintenance Isolation fault Flood 15% 7% 8% 8% 15% 15% 8% 8% 8% 8% Fuego o explosión Contaminación del aceite por gas Rayos

Fallo aislamiento Fallo de diseño, material, humano Sobrecarga Mantenimiento inadecuado Inundación Conexiones sueltas Desconocido Fire or explosion Oil contamination from gas Lighting (thunderstorm) Isolation fault Design, material, or human fault Overload Inadequate maintenance Flood Loose connections Unknown Clase de equipo: Localización: Marca: Matricula: Trans formador 132 kV Subes tación Sadurní MWA Trans former 132 kV T663539

2008 2009 2010 2011 2012 correctivoCorrective preventivoPreventive

Equipment type: Location: Brand: ID: Clase de equipo: Localización: Marca: Matricula: Día Temperatura (Cº) 1 25 2 35 3 25 4 42 5 38 6 25 7 25 8 34 9 27 10 42 11 25 12 44 13 52 14 63 Trans formador 132 kV Subes tación Sadurní MWA Trans former 132 kV T663539 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Te m pe ra tur a Equipment type: Location: Brand: ID: T e m p e ra tu re Day Temperature _(Cº)

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Failure probability Failure forecast

Asset management

Information model

Clase de equipo: Marca:

Tra ns forma dor 132 kV MWA Tra ns former 132 kV

0% 20% 40% 60% 80% 100% 2009 2010 2011 2012 D is poni bi lida d (% ) Equipment type: Brand: A v a il a bi li ty ( % ) Availability 97% 94% _88% 03% 06% _12% 00% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

6 meses 12 meses 24 meses

P rob abil id ad (% )

Estimación tasa de fallo a partir de información de vibraciones

Probabilidad de fallo Probabilidad de no fallo

Pr o b a b il ity (% )

Failure probability Non-Failure probability Estimation of the failure probability from

information regarding vibrations

6 months 12 months 24 months

99.70% 97% 98% 97% 98% 97% 93.50% 00% 03% 02% 03% 02% 03% 07% 75.00% 80.00% 85.00% 90.00% 95.00% 100.00% Velocidad de rotación del generador Nivel de vibraciones del generador Tensión en el generador Calentamiento del generador Densidad del aceite Temperatura del aceite Total

Tasa de fallo generador (información para detección): seis meses

Probabilidad de no fallo Probabilidad de fallo

Temperature (Cº) Temperature F a il u re p ro b a b il it y

Transformer failure rate (information for detection) 6 months

Non-Failure probability Failure probability Total Oil temperature Oil density Transformer warming Transformer voltage Transformer vibrations Transformer rotation speed

Transformer B9389AU failure rate: 6 months

Non-Failure probability Failure probability Total Humidity Lighting (thunderstorm) Loose connections Flood Inadequate maintenance Over-voltage in line Overload Design, material, or human fault Isolation fault Unknown Oil contamination from gas Fire or explosion

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Maintenance strategies

Asset management

Information model

140,117 33,018 33,018 33,018 357,102 240,827 152,237 194,929 194,929 194,929 0 100,000 200,000 300,000 400,000 500,000 600,000

Tradicional Preventivo + Analítico Preventivo modificado (*) + analítico Modelo analítico C o st e d e d ie z tr an sfo rm ad o re s d e t e n si ó n d e 66 -45 kV d u ran te t re s añ o s (€ )

Manteniento derivado modelo analítico Mantenimiento preventivo Mantenimiento correctivo

Traditional Preventive + predictive Modified preventive + predictive

Predictive

Predictive maintenance Preventive maintenance Corrective maintenance

M a in te n a n c e c o s t (€ ) Clase de equipo: Localización: Marca: Matricula: T663539 Trans formador 132 kV Subes tación Sadurní MWA Trans former 132 kV

83% 80% 77% _73%

17% 20% 23% 27%

6 meses 12 meses 18 meses 24 meses

Situación actual

Probabilidad Fallo Probabilidad no fallo

91% _86% _84% _83%

9% _14% _16% _17%

Sustitución de cambiador de tomas

90% _85% _82% _81%

10% _15% _18% _19%

Sustitución de bornas

Equipment type: Location: Brand: ID:

6 months 12 months 18 months 24 months

Failure probability Non-Failure probability

Current situation

Tap changers replacement

Bollard replacement

What if assessment

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A comparative analysis between carrying out the

maintenance according to traditional models and

the result of applying new predictive models of

equipment behavior as a basis for the process

Analyzed case:



Distribution transformer (132 kV)



Daily information about 10 similar transformers has been

gathered for the last three years:

temperature, gas pressure and

gas content in oil



Annual maintenance cost for one of the transformer:

25,083

€ per

year.

This figure includes corrective and preventive maintenance

(8,974 € from preventive maintenance)

Assessment

An example of the use of Analytic models

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Predictive model:

Weibull distribution.



It is a probability distribution which allow us to calculate

the

failure probability by some relevant variables of the transformer.

The cumulative distribution function for the Weibull distribution

is:

Where:



x: a relevant variable



𝛾

: location parameter: It indicates the «location» of the function or

the first value of x which makes the function different to 0: the origin

of the function



β:

scale parameter or characteristic life (value for the variable (x-γ)

for which the failure probability is 63%)



𝛼

:

shape parameter: It determines the function shape

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Predictive model:

Weibull distribution.

Transformers

Día Temperatura (Cº) Fallo 0 70 No 1 73 No 2 61 No 31 85 No 32 91 Si 10950 76 No Day Temperature (Cº) Failure Temperatura (Cº) Fallo Ordinal 70 Sí 1 89 Sí 3 91 Sí 4 99 Sí 6 102 Sí 7 103 Sí 8 109 Sí 9 111 Sí 11 112 Sí 12 113 Sí 13 114 Sí 15 Total 15 Temperature (Cº) Failure Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

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Weibull Distribution for Gas Content in Oil

Weibull Distribution for Temperature

Weibull Distribution for Gas Pressure

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Exhaustive monitoring

Maintenance action

Important damage to equipment

60 70 80 90 100 110 120 130 140

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Time

115

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Number of actions per year (hours)

Unavailabity time

Impact on availabilty in extreme

situations

Annual cost (€)

Transformers

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Weibull function for failures due to temperature of the transformer

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Time series

ARIMA and SARIMA

models will be used to predict the evolution of the variables according to historical data on a time horizon.

Failure probabilities

associated to the values given by the Weibull model will be obtained.

• ARIMA model (6,0,3)

An example of the use of Analytic models

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Ten transformers:

 Equipment historic data:

 Preventive maintenance.  Corrective maintenance.  Maintenance costs.  Downtimes  Predictive model simulation Management models including preventive and predictive maintenance are simulated.

Stress case

In case the Analytic model, by means of variables on-going monitoring, could detect a likely critical components failure, the avoided breakdown:

 Its repair cost would vary from 250,000 to 400,000 €.

 A downtime of 30 days. Comparation (€) Traditional Preventive + Predictive Variation Costs of a maintenance action 382,859 360,956 21,903 Comparation (€) Traditional Preventive + Predictive Variation Costs of a maintenance action 382,859 175,519 207,339

An example of the use of Analytic models

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